Consider alternatives to AQL sampling plans

There are many alternatives to AQL sampling plans, but most companies are not aware of them to try to explore them. Acceptance Quality Limit, or AQL, is applied as a yardstick in most manufacturing organizations for assessing the quality of products they purchase. Its importance can be gauged from the fact that it is only after the Quality department is convinced about the product’s ability to meet AQL that the receipt is acknowledged and the payment made.

AQL

Understanding AQL

AQL, short for Acceptance Quality Limit, is the smallest, or worst, or lowest level of tolerable process means that can be accepted for the quality of product. It is the ratio or percentage level below which it is not possible to degrade quality to be termed acceptable.

Most medical device companies accept Acceptance Quality Level as a standard business practice and criteria for quality. The attribute sampling based on ANSI/ASQ Z1.4 and Zero Acceptance Number Sampling Plans developed by Nicholas L. Squeglia are the most common applications used by companies.

Are there viable alternative to AQL sampling plans?

Yes, these two methods mentioned above are very commonly used, but it does not mean that they are the best. These methods are effective, but are not sufficient by themselves. Medical devices need to be aware of a variety of methods and when and how to use them.

The ISO 9001 and ISO 13485 require companies to establish “processes needed to demonstrate [product] conformity”. The FDA’s GMP (21CFR820) also requires that “sampling methods are adequate for their use”. An FDA guideline further states that “[a] manufacturer shall be prepared to demonstrate the statistical rationale for any sampling plan used”.

Sampling Methods

An AQL sampling plan does not provide everything that is needed to meet either or all of those requirements. Using only Attribute sampling based on ANSI/ASQ Z1.4 and Squeglia’s Zero Acceptance Number Sampling Plans, it is not possible to actually “demonstrate” that an AQL sampling plan ensures product quality.

This is where “Confidence/reliability” calculations come in as alternatives to AQL sampling plans. They are a more comprehensive and effective way of assessing the quality of purchased parts. Making calculations using tables and/or an electronic spreadsheet becomes easier. Using confidence/reliability calculations to provide evidence of product quality is also simplified through this method. The statistical rationale for such calculations is easy to explain and demonstrate, which is why these calculations are considered strong and reliable alternatives to AQL sampling plans.

Get to understand everything about the alternatives to AQL sampling plans

An understanding of these alternatives to AQL sampling plans will be offered at a webinar that is being organized by Compliance4All, a leading provider of professional trainings for all the areas of regulatory compliance. The speaker at this session is John N. Zorich, a senior consultant for the medical device manufacturing industry. To enroll for this highly useful webinar, please visit http://www.compliance4all.com/control/w_product/~product_id=501388LIVE?Wordpress-SEO

In-depth understanding of the alternatives to AQL sampling plans

John Zorich will explain the pros and cons of ANSI Z1.4, and Squeglia’s C=0 in detail. He will explain the areas in which these plans fall short of meeting regulatory requirements. He will offer real-world examples of how using such sampling plans leads to production of non-conforming product to augment the learning on the alternatives to AQL sampling plans.

The ISO and FDA regulations and guidelines regarding the use of statistics, especially in regards to Sampling Plans, will be examined. As part of alternatives to AQL sampling plans, John will explain the advantages of “confidence/reliability” calculations. Such calculations are demonstrated for Attribute data (pass/fail, yes/no data) as well as for variables data (i.e., measurements). If variables data is “Normally distributed”, the calculations are extremely simple. The speaker at this webinar will explain how “non-Normal” data need to be handled, and will providing the methods, formulas, and tools to handle such situations.

The webinar on alternatives to AQL sampling plans will conclude with a discussion of how one OEM manufacturer has implemented “confidence/reliability” calculations instead of AQL sampling plans for all of its clients. The speaker will offer suggestions for how to use “confidence/reliability” QC specifications instead of “AQL” QC specifications. The use of “reliability plotting” for assessing product reliability during R&D is also discussed.

The speaker will cover the following areas at this webinar:

  • AQL and LQL sampling plans
  • OC Curves
  • AOQL
  • ANSI Z1.4
  • Squeglia’s C=0
  • Confidence/Reliability calculations for
    • Attribute data
    • Normally-distributed variables data
    • Non-Normal data
  • Transformations to Normality
  • K-tables
  • Normal Probability Plot
  • Reliability Plotting.
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